Neurofilament light chain in serum of cancer patients with acute neurological complications.

Aim: Neurofilament light chain (NfL) is a nonspecific sensitive biomarker of axonal damage.Methods: This case series identified cancer patients with neurological complications who had serum NfL measurements and paired these results to outcomes.Results: NfL serum levels were available in 15 patients with hematological malignancies or solid tumors. The neurological complications studied were immune effector cell-associated neurotoxicity syndrome, immune checkpoint inhibitor-related encephalopathy, anoxic brain injury, Guillain-Barre syndrome, hemophagocytic lymphohistiocytosis, transverse myelitis, paraneoplastic syndrome, central nervous system demyelinating disorder and chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids. All patients but one with serum NfL >900 pg/ml died during hospitalization.Conclusion: Serum NfL levels consistently corresponded to death, disease severity or recovery in this series.

[Box: see text].

Emerging Biomedical and Clinical Applications of 3D-Printed Poly(Lactic Acid)-Based Devices and Delivery Systems.

Poly(lactic acid) (PLA) is widely used in the field of medicine due to its biocompatibility, versatility, and cost-effectiveness. Three-dimensional (3D) printing or the systematic deposition of PLA in layers has enabled the fabrication of customized scaffolds for various biomedical and clinical applications. In tissue engineering and regenerative medicine, 3D-printed PLA has been mostly used to generate bone tissue scaffolds, typically in combination with different polymers and ceramics. PLA's versatility has also allowed the development of drug-eluting constructs for the controlled release of various agents, such as antibiotics, antivirals, anti-hypertensives, chemotherapeutics, hormones, and vitamins. Additionally, 3D-printed PLA has recently been used to develop diagnostic electrodes, prostheses, orthoses, surgical instruments, and radiotherapy devices. PLA has provided a cost-effective, accessible, and safer means of improving patient care through surgical and dosimetry guides, as well as enhancing medical education through training models and simulators. Overall, the widespread use of 3D-printed PLA in biomedical and clinical settings is expected to persistently stimulate biomedical innovation and revolutionize patient care and healthcare delivery.

The functional lumen imaging probe contractile response pattern is the best predictor of botulinum toxin response in esophagogastric junction outflow obstruction.

BACKGROUND: Esophagogastric junction outflow obstruction (EGJOO) is a heterogenous disorder in which the correct management strategy is unclear. We assessed whether functional lumen imaging probe (FLIP) topography data could select EGJOO, which would benefit from lower esophageal sphincter Botulinum toxin (Botox) injection. METHODS: This was a single-center prospective study of adult patients meeting Chicago Classification (CC) v3.0 criteria for EGJOO. We assessed differences in pretreatment physiologic measurements on high-resolution manometry (HRM) and FLIP and other relevant clinical variables in predicting Botox response (>50% in BEDQ at 2 months). KEY RESULTS: Sixty-nine patients were included (ages 33-90, 73.9% female). Of these, 42 (61%) were Botox responders. Majority of physiologic measures on HRM and FLIP and esophageal emptying were not different based on Botox response. However, a spastic-reactive (SR) FLIP contractile response (CR) pattern predicted a Botox response with OR 25.6 (CI 2.9-229.6) when compared to antegrade FLIP CR; and OR for impaired-disordered/absent CR was 22.5 (CI 2.5-206.7). Logistic regression model using backward elimination (p value = 0.0001, AUC 0.79) showed that a SRCR or IDCR/absent response and the upright IRP predicted Botox response. Response rates in tiered diagnostic groups were: (i) CCv3.0 EGJOO (60.9%), (ii) CCv4.0 EGJOO (73.1%), (iii) CCv4.0 + FLIP REO (80%), (iv) CCv4.0, FLIP REO, and abnormal FLIP CR (84.2%), and (v) CCv4.0, FLIP REO, and SR FLIP CR (90%). CONCLUSIONS AND INFERENCES: FLIP helps identify patients with EGJOO who are likely to response to LES Botox therapy. An abnormal FLIP contractile response pattern is the single-most important predictor of a Botox response.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease.

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for high-dimensional and spatially distributed covariates to predict continuous outcomes. However surprisingly, there is limited development on corresponding Bayesian classification methods relying on tensor-valued covariates. Standard approaches that vectorize the image are not desirable due to the loss of spatial structure, and alternate methods that use extracted features from the image in the predictive model may suffer from information loss. We propose a novel data augmentation-based Bayesian classification approach relying on tensor-valued covariates, with a focus on imaging predictors. We propose two data augmentation schemes, one resulting in a support vector machine (SVM) type of classifier, and another yielding a logistic regression classifier. While both types of classifiers have been proposed independently in literature, our contribution is to extend such existing methodology to accommodate high-dimensional tensor valued predictors that involve low rank decompositions of the coefficient matrix while preserving the spatial information in the image. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for implementing these methods. Simulation studies show significant improvements in classification accuracy and parameter estimation compared to routinely used classification methods. We further illustrate our method in a neuroimaging application using cortical thickness MRI data from Alzheimer's Disease Neuroimaging Initiative, with results displaying better classification accuracy throughout several classification tasks, including classification on pairs of the three diagnostic groups: normal control, AD patients, and MCI patients; gender classification (males vs females); and cognitive performance based on high and low levels of MMSE scores.

Multi-task Learning with High-Dimensional Noisy Images.

Recent medical imaging studies have given rise to distinct but inter-related datasets corresponding to multiple experimental tasks or longitudinal visits. Standard scalar-on-image regression models that fit each dataset separately are not equipped to leverage information across inter-related images, and existing multi-task learning approaches are compromised by the inability to account for the noise that is often observed in images. We propose a novel joint scalar-on-image regression framework involving wavelet-based image representations with grouped penalties that are designed to pool information across inter-related images for joint learning, and which explicitly accounts for noise in high-dimensional images via a projection-based approach. In the presence of non-convexity arising due to noisy images, we derive non-asymptotic error bounds under non-convex as well as convex grouped penalties, even when the number of voxels increases exponentially with sample size. A projected gradient descent algorithm is used for computation, which is shown to approximate the optimal solution via well-defined non-asymptotic optimization error bounds under noisy images. Extensive simulations and application to a motivating longitudinal Alzheimer's disease study illustrate significantly improved predictive ability and greater power to detect true signals, that are simply missed by existing methods without noise correction due to the attenuation to null phenomenon.

Prognostic value of tumor volume doubling time in lung-metastatic adenoid cystic carcinoma.

OBJECTIVES: Lung metastases in adenoid cystic carcinoma (ACC) usually have indolent growth and the optimal timing to start systemic therapy is not established. We assessed ACC lung metastasis tumor growth dynamics and compared the prognostic value of time to progression (TTP) and tumor volume doubling time (TVDT). METHODS: The study included ACC patients with ≥1 pulmonary metastasis (≥5 mm) and at least 2 chest computed tomography scans. Radiology assessment was performed from the first scan showing metastasis until treatment initiation or death. Up to 5 lung nodules per patient were segmented for TVDT calculation. To assess tumor growth rate (TGR), the correlation coefficient (r) and coefficient of determination (R2) were calculated for measured lung nodules. TTP was assessed per RECIST 1.1; TVDT was calculated using the Schwartz formula. Overall survival was analyzed using the Kaplan-Meier method. RESULTS: The study included 75 patients. Sixty-seven patients (89%) had lung-only metastasis on first CT scan. The TGR was overall constant (median R2 = 0.974). Median TTP and TVDT were 11.2 months and 7.5 months. Shorter TVDT (<6 months) was associated with poor overall survival (HR = 0.48; p = 0.037), but TTP was not associated with survival (HR = 1.02; p = 0.96). Cox regression showed that TVDT but not TTP significantly correlated with OS. TVDT calculated using estimated tumor volume correlated with TVDT obtained by segmentation. CONCLUSION: Most ACC lung metastases have a constant TGR. TVDT may be a better prognostic indicator than TTP in lung-metastatic ACC. TVDT can be estimated by single longitudinal measurement in clinical practice.

Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data.

There is a growing body of literature on knowledge-guided statistical learning methods for analysis of structured high-dimensional data (such as genomic and transcriptomic data) that can incorporate knowledge of underlying networks derived from functional genomics and functional proteomics. These methods have been shown to improve variable selection and prediction accuracy and yield more interpretable results. However, these methods typically use graphs extracted from existing databases or rely on subject matter expertise, which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian modeling framework to account for network noise in regression models involving structured high-dimensional predictors. Specifically, we use 2 sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed predictors in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian regression model with structured high-dimensional predictors involving an adaptive structured shrinkage prior. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of a genomics dataset and another proteomics dataset for Alzheimer's disease.

Functional lumen imaging probe topography identifies patients with normal acid exposure and esophageal hypervigilance amongst proton-pump inhibitor non-responders.

BACKGROUND: Multiple factors contribute to symptom generation and treatment response in proton-pump inhibitor non-responders (PPI-NRs). We aimed to test whether PPI-NRs with normal acid exposure have a higher degree of esophageal hypersensitivity and hypervigilance and can be identified using functional lumen imaging probe (FLIP) topography at the time of endoscopy. METHODS: Data from PPI-NRs whom underwent endoscopy, FLIP and wireless 96-h pH-metry were retrospectively analyzed. Patients were grouped according to acid exposure time (AET) as (a) 0 days abnormal (AET > 6%), (b) 1-2 days abnormal, or (c) 3-4 days abnormal. The esophageal hypervigilance and anxiety scale (EHAS) score and other symptom scores were compared between groups. The discriminatory ability of the esophagogastric junction (EGJ) distensibility index (DI) and max EGJ diameter in identifying patients with 0 days abnormal AET was tested via receiver-operating-characteristic (ROC) curve analysis. RESULTS: EHAS score was 38.6 in the 0 days abnormal AET group, 30.4 in the 1-2 days abnormal AET group (p = 0.073 when compared to 0 days abnormal) and 28.2 in the 3-4 days abnormal AET group (p = 0.031 when compared to 0 days abnormal). Area-under-the-curve (AUC) for the DI in association with 0 days AET > 6% was 0.629. A DI of < 2.8 mm2/mmHg had a sensitivity of 83.3%, and negative predictive value of 88% in classifying patients with 0 days abnormal acid exposure (p = 0.004). CONCLUSIONS: FLIP complements prolonged wireless pH-metry in distinguishing the subset of PPI-NRs with completely normal acid exposure and a higher burden of esophageal hypervigilance. Proper identification of patients along the functional heartburn spectrum can improve overall surgical outcomes.

Safety and efficacy of a new high-dose regimen of panobinostat, gemcitabine, busulfan, and melphalan for 1st or 2nd salvage ASCT for refractory/relapsed or high-risk myeloma: Matched-pair comparisons with concurrent control cohorts.

Improvement of autologous stem-cell transplantation (ASCT) for myeloma is needed. Building on our prior work, we prospectively evaluated panobinostat and gemcitabine/busulfan/melphalan (GemBuMel) with ASCT in this population. Patients aged 18-65 years with relapsed/refractory or high-risk myeloma and adequate end-organ function were eligible. Treatment included panobinostat (20 mg/day, days -9 to -2) and GemBuMel (days -8 to -2). Patients were enrolled in 1st (ASCT-1) or 2nd ASCT (ASCT-2) cohorts. We compared their outcomes with all our other concurrent ASCT patients who met eligibility criteria but received melphalan or BuMel off study, matched for age, prior therapy lines, high-risk cytogenetics, and response at ASCT. We enrolled 80 patients, 48 and 32 in the ASCT-1 and ASCT-2 cohorts, respectively; in these two cohorts, high-risk cytogenetics were noted in 33 and 15 patients, respectively; unresponsive disease in 12 and 11 patients, respectively, after a median of 2 and 3 therapy lines, respectively. Transplant-related mortality (TRM) occurred in two ASCT-2 patients. One-year PFS rates were 69% (ASCT-1) and 72% (ASCT-2); 1-year OS rates were 79% (ASCT-1) and 84% (ASCT-2). Minimal residual disease negativity improved after ASCT-1 (8.5%-23%, p < .0001) and ASCT-2 (34%-55%, p = .02), which correlated with improved outcomes. Trial patients and controls (N = 371) had similar TRM and post-ASCT maintenance. Trial patients had better PFS after either a 1st (p = .02) or a 2nd ASCT (p = .04) than matched-paired control patients. In conclusion, panobinostat/GemBuMel is effective for relapsed/refractory or high-risk myeloma patients, with better PFS than concurrent matched controls receiving melphalan or BuMel.

Bayesian longitudinal tensor response regression for modeling neuroplasticity.

A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.

Integrative Bayesian tensor regression for imaging genetics applications.

Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic approaches that combine data across modalities such as imaging and genetics. However, there are limited methods in literature that are able to systematically combine high-dimensional voxel-level imaging and genetic data for accurate prediction of clinical outcomes of interest. Existing prediction models that integrate imaging and genetic features often use region level imaging summaries, and they typically do not consider the spatial configurations of the voxels in the image or incorporate the dependence between genes that may compromise prediction ability. We propose a novel integrative Bayesian scalar-on-image regression model for predicting cognitive outcomes based on high-dimensional spatially distributed voxel-level imaging data, along with correlated transcriptomic features. We account for the spatial dependencies in the imaging voxels via a tensor approach that also enables massive dimension reduction to address the curse of dimensionality, and models the dependencies between the transcriptomic features via a Graph-Laplacian prior. We implement this approach via an efficient Markov chain Monte Carlo (MCMC) computation strategy. We apply the proposed method to the analysis of longitudinal ADNI data for predicting cognitive scores at different visits by integrating voxel-level cortical thickness measurements derived from T1w-MRI scans and transcriptomics data. We illustrate that the proposed imaging transcriptomics approach has significant improvements in prediction compared to prediction using a subset of features from only one modality (imaging or genetics), as well as when using imaging and transcriptomics features but ignoring the inherent dependencies between the features. Our analysis is one of the first to conclusively demonstrate the advantages of prediction based on combining voxel-level cortical thickness measurements along with transcriptomics features, while accounting for inherent structural information.

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation.

BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS: Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS: After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean ± standard deviation: -0.97 ± 0.20), sarcoma (-1.04 ± 0.54), H&N (-1.00 ± 0.24), and GI (-2.95 ± 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.

High-Dose Chemotherapy and Autologous Stem Cell Transplantation for Relapsed or Refractory Primary Mediastinal Large B-Cell Lymphoma.

Primary mediastinal large B-cell lymphoma (PMBCL) is an uncommon, aggressive type of non-Hodgkin lymphoma. Rituximab-containing chemoimmunotherapy with or without radiation therapy (RT) is standard first-line treatment. Relapsed or refractory (R/R) disease has long been treated with salvage chemotherapy followed by high-dose chemotherapy (HDC), with autologous stem cell transplantation (ASCT) in appropriate patients. We retrospectively analyzed all patients with R/R PMBCL treated with HDC/ASCT at our center between January 2000 and August 2022. The 60 study patients received either rituximab-BEAM (n = 37) or rituximab-gemcitabine/busulfan/melphalan (R-GemBuMel) with or without vorinostat (n = 23), followed by ASCT. Forty-six patients received mediastinal RT, either as prior consolidation of frontline therapy or following ASCT. At median follow-up of 6 years (range, .3 to 21 years), the 5-year progression-free survival (PFS) and overall survival (OS) rates of the whole group were 58% and 77%, respectively, for the entire cohort, 51% and 65% for the R-BEAM recipients, and 69% and 82% for R-vorinostat/GemBuMel recipients. Multivariable analyses showed that a negative positron emission tomography scan at ASCT (hazard ratio [HR], .28) and involvement of only 1 organ (HR, .33) were independently associated with improved PFS. In addition, receipt of R-vorinostat/GemBuMel (HR, .23) was an independent favorable predictor of OS. Our data indicate that HDC/ASCT is effective in R/R PMBCL, with improved outcomes in patients receiving R-vorinostat/GemBuMel.

Incidence of resting-state functional connectivity to secondary language areas and contralateral language areas in patients with brain tumors.

PURPOSE: Secondary language areas, including the pre-supplementary motor area (pre-SMA), dorsolateral prefrontal cortex (DLPFC), and the visual word form area (VWFA) play important roles in speech, but have been under-evaluated in the realm of resting-state (rs)-fMRI. The purpose of this study is to determine the incidence that secondary language areas and contralateral language areas can be localized using seed-based correlation (SBC) rs-fMRI. METHODS: We retrospectively reviewed 40 rs-fMRIs for functional connectivity (FC) to secondary language areas in cases where FC to Broca's or Wernicke's area near tumor in the left hemisphere were successfully generated using SBC analysis. Logistical regression was used for statistical analysis. RESULTS: SBC rs-fMRI with a seed in the left Broca's or Wernicke's area ipsilateral to the tumor was performed in the 40 patients. 72.5% of cases showed FC to the left DLPFC, 67.5% to left pre-SMA, and 52.5% of cases had FC to right Broca's area. In addition to other correlations, we found older patients have a lower incidence of FC to the right Wernicke's area when seeded from both left Broca's and left Wernicke's area (p-value = .016, odds ratio = 0.94). CONCLUSION: SBC rs-fMRI can detect left hemispheric secondary language areas as well as right hemispheric primary and secondary language areas. The left DLPFC showed the highest incidence of FC, followed by the left pre-SMA when seeded from both left Broca's and Wernicke's area. Logistics regression also showed in some instances, differences in the incidence of FC to language areas was dependent on age, seed location, and gender.

Accounting for temporal variability in functional magnetic resonance imaging improves prediction of intelligence.

Neuroimaging-based prediction methods for intelligence have seen a rapid development. Among different neuroimaging modalities, prediction using functional connectivity (FC) has shown great promise. Most literature has focused on prediction using static FC, with limited investigations on the merits of such analysis compared to prediction using dynamic FC or region-level functional magnetic resonance imaging (fMRI) times series that encode temporal variability. To account for the temporal dynamics in fMRI, we propose a bi-directional long short-term memory (bi-LSTM) approach that incorporates feature selection mechanism. The proposed pipeline is implemented via an efficient algorithm and applied for predicting intelligence using region-level time series and dynamic FC. We compare the prediction performance using different fMRI features acquired from the Adolescent Brain Cognitive Development (ABCD) study involving nearly 7000 individuals. Our detailed analysis illustrates the consistently inferior performance of static FC compared to region-level time series or dynamic FC for single and combined rest and task fMRI experiments. The joint analysis of task and rest fMRI leads to improved intelligence prediction under all models compared to using fMRI from only one experiment. In addition, the proposed bi-LSTM pipeline based on region-level time series identifies several shared and differential important brain regions across fMRI experiments that drive intelligence prediction. A test-retest analysis of the selected regions shows strong reliability across cross-validation folds. Given the large sample size of ABCD study, our results provide strong evidence that superior prediction of intelligence can be achieved by accounting for temporal variations in fMRI.

Comparative Analyses of the Clinicopathologic Features of Short-Term and Long-Term Survivors of Patients with Pancreatic Ductal Adenocarcinoma Who Received Neoadjuvant Therapy and Pancreatoduodenectomy.

Neoadjuvant therapy (NAT) is increasingly used to treat patients with pancreatic ductal adenocarcinoma (PDAC). Patients with PDAC often show heterogenous responses to NAT with variable clinical outcomes, and the clinicopathologic parameters associated with these variable outcomes remain unclear. In this study, we systematically examined the clinicopathologic characteristics of 60 short-term survivors (overall survival < 15 months) and 149 long-term survivors (overall survival > 60 months) and compared them to 352 intermediate-term survivors (overall survival: 15-60 months) of PDAC who received NAT and pancreatoduodenectomy. We found that the short-term survivor group was associated with male gender (p = 0.03), tumor resectability prior to NAT (p = 0.04), poorly differentiated tumor histology (p = 0.006), more positive lymph nodes (p = 0.04), higher ypN stage (p = 0.002), and higher positive lymph node ratio (p = 0.03). The long-term survivor group had smaller tumor size (p = 0.001), lower ypT stage (p = 0.001), fewer positive lymph nodes (p < 0.001), lower ypN stage (p < 0.001), lower positive lymph node ratio (p < 0.001), lower rate of lymphovascular invasion (p = 0.001) and perineural invasion (p < 0.001), better tumor response grading (p < 0.001), and less frequent recurrence/metastasis (p < 0.001). The ypN stage is an independent predictor of both short-term and long-term survivors by multivariate logistic regression analyses. In addition, tumor differentiation was also an independent predictor for short-term survivors, and tumor response grading and perineural invasion were independent predictors for long-term survivors. Our results may help to plan and select post-operative adjuvant therapy for patients with PDAC who received NAT and pancreatoduodenectomy based on the pathologic data.

Current Biomedical Applications of 3D-Printed Hydrogels.

Three-dimensional (3D) printing, also known as additive manufacturing, has revolutionized the production of physical 3D objects by transforming computer-aided design models into layered structures, eliminating the need for traditional molding or machining techniques. In recent years, hydrogels have emerged as an ideal 3D printing feedstock material for the fabrication of hydrated constructs that replicate the extracellular matrix found in endogenous tissues. Hydrogels have seen significant advancements since their first use as contact lenses in the biomedical field. These advancements have led to the development of complex 3D-printed structures that include a wide variety of organic and inorganic materials, cells, and bioactive substances. The most commonly used 3D printing techniques to fabricate hydrogel scaffolds are material extrusion, material jetting, and vat photopolymerization, but novel methods that can enhance the resolution and structural complexity of printed constructs have also emerged. The biomedical applications of hydrogels can be broadly classified into four categories-tissue engineering and regenerative medicine, 3D cell culture and disease modeling, drug screening and toxicity testing, and novel devices and drug delivery systems. Despite the recent advancements in their biomedical applications, a number of challenges still need to be addressed to maximize the use of hydrogels for 3D printing. These challenges include improving resolution and structural complexity, optimizing cell viability and function, improving cost efficiency and accessibility, and addressing ethical and regulatory concerns for clinical translation.